Allopathic (MD) and Osteopathic (DO) Performance on the American Board of Physical Medicine and Rehabilitation Initial Certifying Examinations
Bibliographic record
Abstract
BACKGROUND: Osteopathic physicians (DOs) represent over 30% of residents in allopathic (MD) Accreditation Council for Graduate Medical Education (ACGME) accredited physical medicine and rehabilitation (PM&R) training programs. However, some have questioned the quality of osteopathic medical school training and the graduates of osteopathic medical schools. The performance of osteopathic physicians in allopathic PM&R training programs has not been assessed. OBJECTIVE: To compare allopathic (MD) and osteopathic (DO) physician performance on American Board of Physical Medicine and Rehabilitation (ABPMR) initial certifying examinations. DESIGN: Retrospective cross-sectional study. SETTING: Board-eligible PM&R physicians. PARTICIPANTS: MDs and DOs who completed an allopathic ACGME-accredited PM&R residency training program. METHODS: MD and DO pass rates and mean scaled scores on the ABPMR initial certifying examinations were compared. MD versus DO degrees and training program 6 years aggregate board pass rates were independent variables. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: MD and DO pass rates and mean scaled scores on the ABPMR initial certifying examinations. RESULTS: Of the 2187 physicians who were first-time ABPMR initial certifying examination takers, there were 1596 MDs (73%) and 591 DOs (27%). No statistically significant difference was found in pass rates between MDs and DOs on Part I (94.9% vs. 93.9%, P = .35) or Part II (87.8% vs. 88%, P = .83) of the ABPMR certifying examination. Analysis of mean scaled scores demonstrated higher MD scores on both Part I ( 526, SD = 31, vs. 516, SD = 67, P = .002) and Part II ( 6.73, SD = .83 vs. 6.62, SD = .77, P = .005), significant only in programs with a 90%-100% pass rate. These differences, however, were of very small magnitude and likely not meaningful from a clinical or educational perspective. CONCLUSION: This study did not find meaningful differences in performance on the ABPMR certifying examinations between MDs and DOs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".